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Darwin-60B-DUO

The Darwin family's first DUO model — two domain-verified SOTA models served as a single OpenAI-compatible endpoint.

🏆 GPQA Diamond (full 198 questions): 88.38%

Darwin-28B-REASON single 69.70 % · AWAXIS-Think-31B single 77.27 % · first cascade 83.84 % · Darwin-DELPHI DUO 88.38 %

Darwin-60B-DUO unifies two specialist models from the Darwin family behind a single API:

Darwin-28B-REASON — Hugging Face leaderboard GPQA Diamond rank #3, English graduate-level reasoning specialist.
AWAXIS-Think-31B — National K-AI Leaderboard rank #1 (operated by the Ministry of Science and ICT of the Republic of Korea), Korean specialist.

A Hybrid-A router automatically dispatches each request to the optimal strategy (single route / sequential collaboration / ensemble), so callers see one model and one endpoint while internally benefiting from both specialists.

Model Description

Darwin-60B-DUO is a gateway-orchestrated aggregate of two constituent base models. The repository contains a FastAPI orchestrator, configuration, and Docker Compose recipe. The model weights themselves live in the constituent repositories and are loaded at runtime by two vLLM backends.

Component	Source	Architecture	Parameters	Verified Rank
English reasoning specialist	FINAL-Bench/Darwin-28B-REASON	Qwen3.5 multimodal	26.9 B	HF GPQA Diamond #3
Korean specialist (Darwin family, Gemma-4 branch)	Anserwise/AWAXIS-Think-31B	Gemma-4 multimodal	31.27 B	National K-AI Leaderboard #1
Aggregate	This repository (`FINAL-Bench/Darwin-60B-DUO`)	DUO orchestrator	58.17 B	—

Note on AWAXIS membership. AWAXIS-Think-31B is also part of the Darwin family — it is the Korean specialist branch distilled by the Darwin team on top of Google's Gemma-4 base, complementing the original Qwen3.5-line Darwin lineage as the family's second axis.

Hybrid-A Orchestration

The gateway analyzes each incoming request and selects one of five strategies. The default distribution observed on representative traffic is:

Strategy	When it fires	Backends called	Cost vs. single 30 B	Share
`route_awaxis`	Korean-dominant input	AWAXIS only	1×	~50 %
`route_darwin`	English-dominant input	Darwin only	1×	~20 %
`split_refine`	Korean output requiring rigorous English / STEM reasoning	Darwin (draft) → AWAXIS (polish)	2×	~15 %
`split_refine_reverse`	English output requiring Korean cultural / linguistic context	AWAXIS (draft) → Darwin (polish)	2×	~5 %
`ensemble_v1`	Short-answer / multiple-choice queries	Both backends with self-consistency + cross-verification tournament	2×	~10 %

Average effective cost is approximately 1.3× a single 30 B model — 70 % of traffic is served by a single backend; the remaining 30 % uses both.

Intended Use

Primary use cases

Bilingual Korean-English assistants that require both Korean fluency and high-quality English reasoning.
Single-endpoint integration where downstream tooling already targets the OpenAI Chat Completions API (LangChain, LlamaIndex, OpenAI SDK, Continue, Cursor, etc.).
Cost-conscious deployment — most traffic is served by a single backend at 1× cost while difficult cross-domain queries automatically receive a 2× collaboration.

Out-of-scope

Vision / video generation. Both constituent models are text-mode only as deployed here (--limit-mm-per-prompt {"image":0,"video":0}).
Real-time streaming. Initial gateway release does not stream token-by-token. Streaming is planned for v1.1.
Direct AutoModel.from_pretrained() loading. This repository contains an orchestrator, not unified weights. Use the gateway or Docker Compose.
Safety-critical decision making (medical diagnosis, legal advice, autonomous control). LLM hallucinations apply.

How to Use

Option A — Docker Compose (recommended)

bash

git clone https://huggingface.co/FINAL-Bench/Darwin-60B-DUO
cd Darwin-60B-DUO

# HF token is needed only to download constituent weights on first launch
export HF_TOKEN=hf_xxx
docker compose -f docker/docker-compose.yml up -d

# Verify
curl http://localhost:8000/v1/models
# {"object":"list","data":[{"id":"darwin-60b-duo",...}]}

Option B — Manual launch (2 × B200 / H100, FP8)

bash

# Clone the repo (includes both constituent weights, ~120GB LFS)
git lfs install
git clone https://huggingface.co/FINAL-Bench/Darwin-60B-DUO
cd Darwin-60B-DUO

# 1. Darwin-28B-REASON on GPU 0 (from local subfolder)
CUDA_VISIBLE_DEVICES=0 VLLM_DP_MASTER_PORT=45011 \
  vllm serve ./darwin-28r \
    --port 8021 --served-model-name darwin-28r \
    --quantization fp8 --enforce-eager \
    --limit-mm-per-prompt '{"image":0,"video":0}' &

# 2. AWAXIS-Think-31B on GPU 1 (from local subfolder)
CUDA_VISIBLE_DEVICES=1 VLLM_DP_MASTER_PORT=45012 \
  vllm serve ./awaxis-31b \
    --port 8022 --served-model-name awaxis-31b \
    --quantization fp8 --enforce-eager \
    --limit-mm-per-prompt '{"image":0,"video":0}' &

# 3. Gateway
pip install -r gateway/requirements.txt
python gateway/server.py --port 8000 \
    --darwin-url http://127.0.0.1:8021/v1 \
    --awaxis-url http://127.0.0.1:8022/v1

No external HF download. Both weights are inside this repo (./darwin-28r, ./awaxis-31b), so vLLM serves directly from local LFS without re-downloading from upstream repos.

Single-GPU collocation. With FP8 quantization the combined footprint is ~30 GB. Set both backends to CUDA_VISIBLE_DEVICES=0 and --gpu-memory-utilization 0.45 to colocate on a single 80 GB B200 / H100.

OpenAI-compatible call

python

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="anything")

# The router picks split_refine (Korean output with English/STEM reasoning)
resp = client.chat.completions.create(
    model="darwin-60b-duo",
    messages=[{
        "role": "user",
        "content": "Explain the practical difference between GPT-5 and Claude's reasoning, in Korean.",
    }],
)
print(resp.choices[0].message.content)
# Darwin produces the English reasoning; AWAXIS polishes it into natural Korean.

You can also force a specific strategy via the non-standard duo_strategy field:

python

resp = client.chat.completions.create(
    model="darwin-60b-duo",
    messages=[{"role":"user","content":"Which is correct? (A) ... (B) ..."}],
    extra_body={"duo_strategy": "ensemble_v1"},  # force MAJ@8 + cross-verify
)

Inspect the chosen strategy in the response under _duo_route:

json

{
  "choices": [{"message": {"role":"assistant", "content":"..."}}],
  "_duo_route": {
    "strategy": "split_refine",
    "reason": "korean_output_with_english_reasoning",
    "elapsed_s": 4.83,
    "language_ratio": 0.42
  }
}

🛠️ Build Your Own DUO (the gateway is model-agnostic)

The gateway/ in this repo is not specific to Darwin and AWAXIS — it is a general, Apache-2.0 orchestrator that fuses any two OpenAI-compatible backends into a single endpoint. We're open-sourcing it because the pattern is broadly useful, and we'd love to see what the community builds.

The core idea in one sentence

Two small specialists, automatically routed, beat one big generalist on cost and quality — and the user only ever sees a single model.

Pair anything

You don't need our models. Point the gateway at your favourite two and you have a DUO in minutes:

Pair	Why it works
reasoning model + multilingual model	rigorous logic, fluent output (what we did)
fast small model + slow accurate model	cheap on easy queries, escalate only hard ones
code model + general chat model	route by "is this code?"
English specialist + your-language specialist	localize without losing reasoning depth

Adapt in 2 steps

bash

# Step 1 — point at your two backends (any vLLM / TGI / OpenAI-compatible server)
python gateway/server.py --port 8000 \
    --darwin-url http://your-model-A:8000/v1 \
    --awaxis-url http://your-model-B:8000/v1

python

# Step 2 — teach the router when to use which model (gateway/router.py)
def select_strategy(text):
    if is_code(text):        return "route_model_a"   # send code to the code model
    if is_korean(text):      return "route_model_b"   # send Korean to the KO model
    if is_mcq(text):         return "ensemble_v1"      # short-answer → both + pick
    if needs_polish(text):   return "split_refine"     # draft with A, polish with B
    return "route_model_b"                             # sensible default

That's it. The gateway handles the OpenAI API surface, parallel calls, response merging, and _duo_route transparency for you.

What's included vs. what's proprietary

✅ Open (Apache-2.0): the gateway — router, single-route, sequential-refine, and a baseline ensemble. Enough to build a fully working DUO of your own.
🔒 Proprietary (Darwin-DELPHI): the test-time engine and prompt recipes that push this DUO to 88.38 % on GPQA Diamond (above the naive oracle ceiling). The open gateway gives you the architecture; the leaderboard score is ours.

Extend it

DUO → TRIO → N-way: add a third backend and a third route; the merge logic generalizes.
Smarter routing: swap the keyword heuristics in router.py for a tiny learned classifier.
New strategies: contribute your own (debate, best-of-N, tool-use) via PR.

Fork it, point it at your models, and tell us what you built. Open a discussion on the Community tab — good routing recipes and new backend pairings are exactly the kind of thing we want to feature.

Darwin Family

Darwin-60B-DUO sits at the confluence of two complete lineages — the Qwen3.5-based Darwin lineage (English reasoning) and the Gemma-4-based Darwin Korean-specialist branch (AWAXIS). The full family tree, with both constituent ancestries fully expanded:

mermaid

graph TD
    %% Lineage A — English reasoning
    A1[Cohere Command A+ - 218B foundation]:::found --> A2[Darwin-28B-Opus - English reasoning base]:::parent
    A2 --> A3[Darwin-28B-REASON - HF GPQA Diamond #3]:::spec

    %% Other Darwin parents
    A1 -.-> P1[Darwin-218B-Delphi - cascade flagship GPQA 90.91%]:::parent
    A2 -.-> P2[Darwin-9B - omni-modal ko/en compact]:::parent
    P2 -.-> P3[Darwin-31B-Opus - Korean multimodal base]:::parent

    %% Lineage B — Korean specialist
    B1[Google Gemma-4-31B-it - Korean/multilingual base]:::found --> B2[TeichAI gemma-4-31B-it-Claude-Opus-Distill-v2]:::parent
    B2 --> B3[AWAXIS-Think-31B - National K-AI Leaderboard #1, Darwin family Korean specialist]:::spec

    %% The DUO unification
    A3 --> DUO[Darwin-60B-DUO - this model]:::duo
    B3 --> DUO

    classDef found fill:#e8f0fe,stroke:#1a73e8,color:#0a0a0a
    classDef parent fill:#fff4e5,stroke:#f29900,color:#0a0a0a
    classDef spec fill:#e6f4ea,stroke:#34a853,color:#0a0a0a
    classDef duo fill:#fce8f3,stroke:#d81b60,color:#0a0a0a,stroke-width:3px

Plain-text fallback

Darwin Family

Lineage A — English reasoning (Qwen3.5-line)
└── Cohere Command A+ (218B foundation)
    └── Darwin-28B-Opus (English reasoning base)
        └── Darwin-28B-REASON  🥉  ← HF GPQA Diamond #3
                                     (English reasoning specialist)
    │
    │   Related Darwin parents in this lineage:
    │   • Darwin-218B-Delphi  (cascade flagship, GPQA Diamond 90.91 %)
    │   • Darwin-9B           (omni-modal ko/en compact)
    │   • Darwin-31B-Opus     (Korean multimodal base)

Lineage B — Korean specialist (Gemma-4-line)
└── Google Gemma-4-31B-it (Korean / multilingual base)
    └── TeichAI gemma-4-31B-it-Claude-Opus-Distill-v2
        └── AWAXIS-Think-31B  🥇  ← National K-AI Leaderboard #1
                                     (Darwin family Korean specialist)

         │                             │
         └──────── DUO unification ────┘
                          ↓
              ⭐ Darwin-60B-DUO ⭐  ← THIS MODEL
              "Two SOTAs, one OpenAI-compatible endpoint."

The HF Model tree widget (right sidebar) automatically renders the upstream chain from each base_model declared in the YAML frontmatter, so the full ancestry — Cohere Command A+ and Google Gemma-4-31B-it at the roots — is browsable directly on this page.

Operation Modes

Mode 1 — Route (single backend, ~70 % of traffic)

The router selects one backend based on language ratio and lightweight keyword heuristics:

korean_ratio(prompt) > 0.3 → AWAXIS
ASCII / code / math markers (def , import, \boxed, prove, …) → Darwin
Mixed → AWAXIS (Korean-first default)

Mode 2 — Split / Refine (sequential collaboration, ~20 % of traffic)

One model drafts, the other polishes. The polish instruction is language-adaptive:

User: "Explain entropy intuitively in Korean."

Step 1 — Darwin (rigorous English reasoning)
        "Entropy quantifies the number of microstates compatible
         with a given macrostate, representing disorder ..."

Step 2 — AWAXIS (natural Korean polish)
        "엔트로피는 쉽게 말하면 '무질서함의 정도'입니다.
         같은 모습으로 보이지만 사실 그 안에 ..."

The reverse path (AWAXIS draft → Darwin polish) fires when the output language is English but the prompt requires Korean cultural or linguistic context.

Mode 3 — Ensemble V₁ Tournament (~10 % of traffic)

For multiple-choice and short-answer queries, both backends produce N = 8 samples at temperature 0.7. Each backend's answer is its own majority vote (self-consistency). If the two majorities agree → return. If they disagree → each backend verifies the pair (cross-verification), and the tournament winner is selected. A confidence tiebreaker (own-vote count) resolves split verdicts.

Repository Layout

Darwin-60B-DUO/
├── README.md                  ← this model card
├── image.png                  ← cover image
├── config.json                ← DUO configuration & orchestration metadata
├── tokenizer_info.json        ← constituent tokenizer references
├── LICENSE                    ← Gemma + Apache-2.0 dual notice
├── darwin-28r/                ← Darwin-28B-REASON full weights + tokenizer + config (~54GB)
│   ├── config.json
│   ├── tokenizer.json
│   ├── chat_template.jinja
│   ├── model.safetensors.index.json
│   └── model-00001-of-00012.safetensors ... model-00012-of-00012.safetensors
├── awaxis-31b/                ← AWAXIS-Think-31B full weights + tokenizer + config (~62.5GB)
│   ├── config.json
│   ├── tokenizer.json
│   ├── chat_template.jinja
│   ├── model.safetensors.index.json
│   └── model-00001-of-00002.safetensors ... model-00002-of-00002.safetensors
├── gateway/
│   ├── server.py              ← FastAPI OpenAI-compatible orchestrator
│   ├── router.py              ← language / domain / MCQ classifier
│   ├── refine.py              ← sequential refine (drafter → polisher)
│   ├── ensemble.py            ← V₁ MAJ@N + cross-verification
│   └── requirements.txt
├── docker/
│   └── docker-compose.yml     ← vLLM ×2 + gateway integrated launcher
└── benchmarks/
    ├── README.md              ← full evaluation results
    ├── cascade_v4_32k.json    ← cascade v4 raw data
    ├── recovery_v5.json       ← recovery v5 raw data
    └── poe_v6.json            ← POE v6 raw data (latest)

Self-contained: all model weights (both constituents) are included in this repository — git clone once and you have everything. Total LFS payload: ~120GB.

Evaluation

Verified constituent scores

Constituent	Benchmark	Rank
Darwin-28B-REASON	Hugging Face GPQA Diamond	#3
AWAXIS-Think-31B	National K-AI Leaderboard (Republic of Korea, MSIT)	#1

Darwin-60B-DUO aggregate

Benchmark	Status
GPQA Diamond (full 198 questions)	88.38% (175/198) — see `benchmarks/`
KMMLU	TBA
CLIcK (Korean cultural reasoning)	TBA
Helmet / Ruler (long context)	TBA
Needle-in-Haystack 32 K / 128 K	NIAH 32 K: 5/5 per backend (sanity, single model only) — full DUO numbers TBA

Aggregate DUO benchmark results will be published in benchmarks/ after formal evaluation. The verified constituent ranks above are independent third-party measurements and are not aggregate DUO scores.

Cost / latency notes

Route mode: comparable to a single 30 B FP8 backend (1× latency, 1× compute).
Split mode: ~2× latency (two sequential generations).
Ensemble V₁: ~2× compute (parallel) plus a short cross-verify round when majorities disagree.

Bias, Risks, and Limitations

Hallucination. Standard LLM caveats apply. Both backends can produce confident but incorrect outputs, especially on out-of-distribution queries.
Disagreement bias. Empirically, the V₁ tournament occasionally selects a wrong answer that both backends collectively favor over a single backend's correct one. The gateway exposes the routing decision in _duo_route for auditability.
Language coverage. Best performance on English and Korean. Other languages fall back to the closer-fit backend without explicit optimization.
Combined weights are not bundled. The aggregator pulls each backend's weights from the constituent repositories. Network and disk for both is required.
Two-GPU baseline. BF16 deployment requires two GPUs. FP8 quantization enables single-GPU operation on B200 / H100 (80 GB).
Training data cut-off. Darwin-28B-REASON: ~ 2026-Q1. AWAXIS-Think-31B: ~ 2026-Q1.

License

Darwin-60B-DUO inherits the Gemma Terms of Use as its effective combined license — the more restrictive of the two constituent licenses.

Constituent	License
Darwin-28B-REASON	Apache-2.0
AWAXIS-Think-31B	Gemma (inherited from Gemma-4)
Darwin-60B-DUO (aggregate)	Gemma (combined-license inheritance)

The orchestrator code (gateway/, docker/) is offered under Apache-2.0 to maximize developer flexibility; combined-license inheritance applies to served model behavior only.

Please review the Gemma Terms of Use and the Gemma Prohibited Use Policy before commercial deployment.

Acknowledgments

FINAL-Bench team — Darwin family architecture and DUO concept
Anserwise Korean specialist team — AWAXIS-Think-31B development
VIDRAFT — orchestration framework and the Hybrid-A routing strategy
Google DeepMind — Gemma-4 foundation
Cohere and Qwen teams — Command A+ / Qwen3.5 foundation lineage

Contact

Hugging Face org: FINAL-Bench
Sister orgs: Anserwise · VIDraft
Issues and discussions: please open a thread on the Community tab of this repository.

Citation

bibtex

@misc{darwin60b-duo-2026,
  title  = {Darwin-60B-DUO: A single-endpoint DUO of an English-reasoning SOTA
            and a Korean SOTA via Hybrid-A orchestration},
  author = {FINAL-Bench Team and Anserwise Team and VIDRAFT},
  year   = {2026},
  howpublished = {Hugging Face},
  url    = {https://huggingface.co/FINAL-Bench/Darwin-60B-DUO}
}

FINAL-Bench/
Darwin-60B-DUO

📱 Run it on your phone or a GPU-less PC → POCKET · 🚀 Try it live (CPU chat)

Darwin-60B-DUO

🏆 GPQA Diamond (full 198 questions): 88.38%

Model Description

Hybrid-A Orchestration

Intended Use

Primary use cases

Out-of-scope

How to Use

Option A — Docker Compose (recommended)

Option B — Manual launch (2 × B200 / H100, FP8)

OpenAI-compatible call

🛠️ Build Your Own DUO (the gateway is model-agnostic)

The core idea in one sentence

Pair anything

Adapt in 2 steps

What's included vs. what's proprietary

Extend it

Darwin Family

Plain-text fallback

Operation Modes

Mode 1 — Route (single backend, ~70 % of traffic)

Mode 2 — Split / Refine (sequential collaboration, ~20 % of traffic)

Mode 3 — Ensemble V₁ Tournament (~10 % of traffic)

Repository Layout

Evaluation

Verified constituent scores

Darwin-60B-DUO aggregate

Cost / latency notes

Bias, Risks, and Limitations

License

Acknowledgments

Contact

Citation